Journal of Economics, Finance and Accounting Studies
type: channel
description: Founded in 2019, the Journal of Economics, Finance and Accounting Studies (JEFAS) is a double-blind peer-reviewed, open-access monthly journal published by Al-Kindi Center for Research and Development. The journal is led by expert international editorial board members who take an objective approach to peer review, ensuring each research paper is reviewed and evaluated on its own scholarly merits and research integrity. Publishing your work in the Journal of Economics, Finance and Accounting Studies means you will receive prompt and reliable publishing and a global platform for your research to reach its full potential. The journal is published in online version only.
publisher: Al-Kindi Center for Research and Development
language: en
publication name: Journal of Economics, Finance and Accounting Studies
issn: 2709-0809
items: Client Happiness as a Predictive Economic Variable in Revenue Systems
items: Driving the Future of Healthcare: Electric Vehicles, Data-Driven Marketing, and Financial Innovation in Pharmaceutical Value Chains
items: A Flexible Elasticity Framework for Measuring Firm Internationalization: Theoretical Model and Simulation-Based Evidence
items: Examining the Origin of Innovation in Decision-Making and Proposing the LOTAR Model
items: Investigating Effective Factors in Application of the Artificial Intelligence in Iran's Food Industry and its Effects on Employment
items: Redefining Healthcare Consumption: AI-Driven Buyer Persona and Financial Transformation
items: Intelligent Pay-by-Bank Architecture: Fraud-Aware Analytics and Risk Modeling for U.S. Account-to-Account Payment Systems
items: Clustering Capital Management Approaches: Evidence from Banking Firms during Economic Disruptions
items: Modeling the Consumer Transition from Cash to Digital Payments and Its Implications for Financial System Efficiency, Transparency, and Inclusion
items: AI-Driven Predictive Modeling for Detecting Suspicious Trading Patterns, Anomalous Order Activity, and Market Manipulation in U.S. Equity Markets
items: Socio-Economic Impacts and Fiscal Vulnerabilities Following the February 6, 2023 Kahramanmaraş Earthquakes in Türkiye
items: AI-Driven Predictive Analytics Framework for Anti-Money Laundering Risk Management and Financial Infrastructure Protection in U.S. Banking Systems
items: Design and Implementation of an AI-Augmented Autonomous Financial Operations Framework for Cloud-Native ERP Systems Using SAP BTP and RAP
items: Generative AI and U.S. Financial Reporting Integrity: Detecting Narrative Manipulation, Risk Disclosure Gaming, and Fraud Signals in 10-K Filings
items: FinTech for Climate Resilience: Measuring Insurance Gaps, Mortgage Stress, and Household Credit Risk in the United States
items: The Use of Technology and Data Analytics in Modern Auditing: A Systematic Review
items: A study of Implicit disagreement in MPC meetings for selected Eastern European Economies using the Taylor Rule
items: A Deterministic Trajectory-Level Evaluation Framework for Learning-Based Agentic Systems
items: Impact of ERP-Enabled Digital Transformation on Cost Accounting Practices in Saudi Firms
items: Machine Learning Models for Detecting Hidden Collusion Networks in U.S. Corporate Finance
items: Real-Time Payments and Real-Time Fraud: A U.S. FinTech Risk Framework for RTP Rails and Consumer Protection
items: Algorithmic Accountability in U.S. Consumer FinTech: Governance Mechanisms for Credit Risk, Fair Lending, and Financial Stability
items: Trade Realignments, Exchange Rate Volatility, and Financial Stability in Sub-Saharan Africa
items: Mega-Sporting Events and Soft Power: Perceived Changes in Qatar’s Nation Brand Following the 2022 FIFA World Cup
items: The Impact of Shanghai Cooperation Organization Membership on Pakistan’s Manufacturing Exports: A Difference-in-Differences Gravity Analysis
items: Bridging Prediction and Profit: Deep Learning models with Trading Evaluation for FTSE 100
items: Trends and Gaps in the Literature about Green Washing in Asia
items: Reconstruction as a Lever for Sustainable Development in Gaza: Between Reality and Prospects for Recovery After the 7 October 2023 War
items: How Smart City Initiatives Economically Affect Small and Medium-Sized Enterprises (SMEs): Innovation Capacity and Competitiveness – A Comparative Study of Japan and the UK
items: Tourism as a Catalyst for Economic Diversification in Saudi Arabia: Vision 2030 and Beyond

Client Happiness as a Predictive Economic Variable in Revenue Systems
type: item
description: In the turbulent environment of the contemporary revenue management systems, the conventional client satisfaction indicators, such as Net Promoter Scores, are less predictive as they do not rely on prospective behavioral factors. This study redefines client happiness as an economic variable with measurement, and puts forward a new composite measure, the Emotional Continuity Index (ECI) a new model of emotionally stable, which predicts repeat engagement, quick decision making and retention. ECI is integrated into Customer Happiness Intelligence System (CHIS), a patent-pending framework based on practical executive practice, which gathers operational data through CRM systems, transaction records, and feedback systems and compiles them with the help of machine learning algorithms in real time to deliver a picture of customer satisfaction levels. The research notes the effectiveness of ECI based on a mixed-methodology which involves conceptual modeling, weighted algorithmic formulation, and 12 months of empirical validation using a stratified sample of 749 clients of Sunlocate Properties (commercial real estate sector): Results show 22% increase in revenue predictability, 15% decrease in churn, and 18% faster decision-making. Such results place client happiness as a measurable managerial dial, and this raises the strength of enterprises in complicated settings. Commercial intelligence and customer-centric design implications, which promote structured analytics to develop flexible, scalable business, can be implied. The piece of writing adds a coherent intellectual journey between the executive experience and technological innovation to close the gaps in the theoretical background and the real-life applications to make the best decisions.
subject: Research Article
  topic: section
subject: Client happiness, revenue predictability, Emotional Continuity Index, commercial intelligence, customer lifecycle management, behavioral analytics
  topic: keywords
creator: Valentin Kulikov
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-29
publication date: 2026-04-29
starting page: 29
ending page: 49
doi: 10.32996/jefas.2026.8.5.4

Driving the Future of Healthcare: Electric Vehicles, Data-Driven Marketing, and Financial Innovation in Pharmaceutical Value Chains
type: item
description: The healthcare and pharmaceutical industries are undergoing a transformative shift driven by electric vehicle (EV) adoption, data-driven marketing, and financial innovation. This paper examines how EV integration in pharmaceutical logistics enhances sustainability, reduces delivery costs, and improves supply chain resilience. It further explores how AI-powered marketing systems and blockchain-based financial mechanisms are reshaping customer engagement and operational transparency. The study synthesizes prior research to present a multi-dimensional framework linking EV infrastructure, digital intelligence, and financial optimization in healthcare value chains.
subject: Research Article
  topic: section
subject: Innovation, Decision-Making, LOTAR Model, Reflective Learning, Cognitive Psychology, Qur’anic Epistemology, Creativity, Educational Management.
  topic: keywords
creator: Tasfia Tarannum
creator: Shanzida Kabir
creator: Md. Nurul Huda Razib
creator: Sabbir Ahammed Khan
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-26
publication date: 2026-04-26
starting page: 25
ending page: 28
doi: 10.32996/jefas.2026.8.5.3

A Flexible Elasticity Framework for Measuring Firm Internationalization: Theoretical Model and Simulation-Based Evidence
type: item
description: This study develops a flexible elasticity framework for measuring firm internationalization. Unlike standard composite indices based on fixed weights, we model internationalization as a heterogeneous elasticity function in which the contribution of each dimension varies across firms. The model generalizes traditional additive approaches by introducing firm-specific elasticities that depend on observable characteristics of the firms. We provide a theoretical characterization of the model and evaluate its properties using simulations. The results show that fixed-weight indices systematically compress heterogeneity and distort firm rankings, whereas the proposed framework preserves structural variation and provides a more consistent representation of internationalization.
subject: Research Article
  topic: section
subject: Firm internationalization
  topic: keywords
subject: heterogeneous firms
  topic: keywords
subject: composite indices
  topic: keywords
subject: measurement bias
  topic: keywords
subject: flexible elasticity
  topic: keywords
subject: simulation
  topic: keywords
creator: Gianmario Strappati
rights: Copyright (c) 2026 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-05-04
publication date: 2026-05-04
starting page: 50
ending page: 65
doi: 10.32996/jefas.2026.8.5.5

Examining the Origin of Innovation in Decision-Making and Proposing the LOTAR Model
type: item
description: In contemporary times, decision-making has become one of the most essential managerial, educational, and social competencies. While decisions occur frequently, they become meaningful and transformative only when they embody elements of creativity and innovation. The fundamental question, therefore, is: Where does innovation in human decision-making originate, and what activates this process within the individual? This study, entitled “Examining the Origin of Innovation in Decision-Making and Proposing the LOTAR Model,” aims to provide a scientific and integrative explanation of innovative decision-making by drawing on cognitive theories and Qur’anic epistemology. Inspired by these principles and integrated with established cognitive frameworks, the proposed LOTAR Model introduces a five-stage dynamic process consisting of Listening, Observation, Thinking, Action, and Reflection. The model argues that innovation does not arise from sudden inspiration, but from the continuous interaction among perception, cognition, experience, and reflective learning. A controlled field experiment was conducted within the Department of Management at Takhar University using a real educational task—improving classroom cleanliness. The results indicated that the LOTAR framework enhances innovation by strengthening Thinking, Action, and Reflection. The findings demonstrate that LOTAR provides a scientifically grounded, culturally contextualized, and Qur’an-informed approach for enhancing reflective thinking and innovative decision-making.
subject: Research Article
  topic: section
subject: Innovation, Decision-Making, LOTAR Model, Reflective Learning, Cognitive Psychology, Qur’anic Epistemology, Creativity, Educational Management.
  topic: keywords
creator: Mohammad Farid Masroor
creator: Rohullah Sajed
creator: Wahidulllah Mowahid
creator: Raqibullah Rasa
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-19
publication date: 2026-04-19
starting page: 09
ending page: 24
doi: 10.32996/jefas.2026.8.5.2

Investigating Effective Factors in Application of the Artificial Intelligence in Iran's Food Industry and its Effects on Employment
type: item
description: The fourth industrial revolution has brought tremendous changes with the introduction of artificial intelligence to various industries. In this study, the effective factors in the use of artificial intelligence in Iran's food industry and its effects on employment have been identified. Due to the growing need to improve the quality of food products, increase productivity, and reduce costs, the use of new technologies, including artificial intelligence, in this industry seems inevitable. This research examines the opportunities and challenges in the use of artificial intelligence and analyzes its effects on employment in Iran's food industry. Investigating these factors in Iran's food industry is of great importance and necessity, and increases productivity, changes in employment, competitiveness, research and development, environmental sustainability, improves food security, and develops appropriate policies. The general goal of investigating these effective factors in the use of artificial intelligence in Iran's food industry is to create a stable and innovative ecosystem that will lead to the improvement of product quality, increase employment, and the realization of sustainable development goals. The type of this research in terms of practical purpose, in terms of descriptive-analytical execution method, and in terms of data collection using studies published until 2023 using PubMed, Cochrane, SCOPUS databases, and Google Scholar search engine with key related words have been searched. This research was selected after a complete review of 30 relevant articles that were consistent with the purpose of the present study. The results show that while artificial intelligence can reduce some traditional jobs, it also creates new job opportunities and increases the need for new skills. In this research, we found that workforce skills and technological infrastructure are of higher importance, government support and organizational culture are of high importance, and costs …
subject: Research Article
  topic: section
subject: Employment, Iran, Artificial Intelligence, Food Industry, Productivity, New Technologies
  topic: keywords
creator: Nabiullah Rafat
creator: Rohullah Sajed
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-15
publication date: 2026-04-15
starting page: 01
ending page: 08
doi: 10.32996/jefas.2026.8.5.1

Redefining Healthcare Consumption: AI-Driven Buyer Persona and Financial Transformation
type: item
description: Artificial Intelligence (AI) is revolutionizing the healthcare industry by reshaping consumer behavior and transforming financial structures. This study examines the evolution of the future healthcare consumer buying persona and evaluates the financial implications of AI integration. The findings suggest that AI-driven personalization, predictive analytics, and digital engagement tools significantly influence consumer decision-making while enhancing operational efficiency and reducing costs for healthcare providers. Additionally, technologies such as blockchain and digital twin systems contribute to transparency and supply chain optimization. The study concludes that AI is fostering a more consumer-centric and financially sustainable healthcare ecosystem.
subject: Research Article
  topic: section
subject: Artificial Intelligence, Healthcare Consumer Behavior, Financial Impact, Digital Health, Personalization, Predictive Analytics
  topic: keywords
creator: Adil Shah
creator: Shanzida Kabir
creator: Sabbir Ahammed Khan
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2024-10-25
publication date: 2024-10-25
starting page: 150
ending page: 153
doi: 10.32996/jefas.2024.6.6.12x

Intelligent Pay-by-Bank Architecture: Fraud-Aware Analytics and Risk Modeling for U.S. Account-to-Account Payment Systems
type: item
description: Pay-by-bank is moving from a niche alternative to a strategically important layer in the U.S. payments landscape. Its appeal is clear: direct account-to-account (A2A) transfers can reduce merchant acceptance costs, accelerate funds availability, support recurring and bill-payment use cases, and create a pathway for open-banking-enabled checkout experiences that do not depend on card rails. At the same time, the same structural strengths that make pay-by-bank attractive—irrevocability, speed, richer data exchange, API connectivity, and multi-party orchestration—also create distinctive fraud, operational, and consumer-protection risks. Unlike card networks, many pay-by-bank implementations cannot rely on mature chargeback mechanisms as a primary safety valve, which makes ex ante risk scoring, identity assurance, behavioral analytics, and post-transaction monitoring more important. This paper develops a framework for an intelligent pay-by-bank architecture for the United States, integrating fraud-aware analytics, explainable machine learning, graph-based entity resolution, sanctions and AML screening, and rail-aware control design across ACH, Same Day ACH, RTP, and FedNow-enabled flows. Using official U.S. sources and contemporary scholarly research, the paper synthesizes the structural drivers of A2A growth, maps the major fraud typologies that threaten consumer and merchant adoption, and proposes a layered analytic design that joins customer risk, payment context, device and channel telemetry, open-banking consent events, account validation, and network intelligence into a unified decision stack. The paper also advances a practical governance model centered on explainability, model-risk management, fairness, vendor oversight, and escalation workflows aligned to high-stakes payment decisions. The discussion argues that successful U.S. pay-by-bank adoption depends not only on rail availability but on trusted orchestration: institutions must combine real-time controls, …
subject: Research Article
  topic: section
subject: Pay-by-bank, account-to-account payments, ACH, Same Day ACH, FedNow, fraud analytics, explainable AI, open banking, synthetic identity, U.S. payment systems
  topic: keywords
creator: Md Nurul Huda Razib
creator: Mohammad Mamun Ur Rashid
creator: Muhammad Helal Uddin
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-18
publication date: 2026-03-18
starting page: 48
ending page: 64
doi: 10.32996/jefas.2026.8.4.4

Clustering Capital Management Approaches: Evidence from Banking Firms during Economic Disruptions
type: item
description: The COVID-19 pandemic significantly disrupted global markets, supply chains, and business operations, leading to adverse effects on firm performance and economic stability. The banking sector, in particular, was compelled to adapt its capital management strategies to remain resilient amid financial uncertainty. Effective capital management—encompassing efficiency, liquidity, and solvency—became critical, as poor financial decisions during crisis periods can threaten organizational survival. This study aims to examine how banking firms respond to financial uncertainty through their capital management practices and to determine how these responses relate to firm profitability. Specifically, it seeks to identify distinct patterns of capital management and assess their implications for financial performance. A quantitative research design was employed using survey data from bank representatives. K-Means Cluster Analysis, utilizing the Lloyd algorithm, was applied to classify firms based on efficiency, liquidity, and solvency. The findings revealed three distinct clusters: efficient-solvent, high-liquid, and safe-player. Results further indicate a significant relationship between capital management approaches and profitability, highlighting the importance of balancing efficiency, liquidity, and solvency. This study contributes to the literature by offering an integrated clustering perspective on capital management and provides practical insights for banking institutions to develop more resilient financial strategies during periods of economic uncertainty.
subject: Research Article
  topic: section
subject: Capital management, business uncertainty, profitability, efficiency, liquidity, solvency
  topic: keywords
creator: Enrico Aurelio L. Torres
creator: Emmanuel P. Paulino
creator: Ronald P. Romero
creator: Paulo Noel A. Mazo
creator: Karina Clara C. Romero
creator: Reynaldo J. Villegas
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 28
ending page: 47
doi: 10.32996/jefas.2026.8.4.3

Modeling the Consumer Transition from Cash to Digital Payments and Its Implications for Financial System Efficiency, Transparency, and Inclusion
type: item
description: This study develops a quantitative framework for modeling the consumer transition from cash to digital payments and for evaluating how that transition affects efficiency, transparency, and financial inclusion. The paper combines evidence from the Federal Reserve’s Diary and Survey of Consumer Payment Choice, the World Bank’s Global Findex Database, Bank for International Settlements research on fast payment systems, and related studies published. The empirical motivation is clear: cash remains important, but U.S. consumers increasingly rely on cards, mobile phones, online banking, and account-to-account rails for purchases, bills, and person-to-person transfers. To interpret this shift, the paper specifies a multi-stage adoption framework in which consumers select payment instruments according to transaction size, acceptance, security, convenience, costs, record keeping, liquidity constraints, and digital access. A transition index and payment-efficiency score are linked to transparency and inclusion outcomes through a comparative design and a U.S.-focused calibration exercise. The results show that movement from cash toward digital payments is neither linear nor uniform. Digital migration is fastest where transaction records, remote commerce, and instant settlement offer visible utility gains. Yet adoption slows for cash-dependent households, privacy-sensitive users, the underbanked, and populations with weaker access to smartphones, broadband, or low-fee accounts. The analysis indicates that payment digitalization improves speed, traceability, and operating efficiency, but inclusion gains depend on governance, affordability, fraud protection, and user-centered design. The main policy implication is that an efficient payment future is not cashless by default; it is plural, interoperable, transparent, and resilient.
subject: Research Article
  topic: section
subject: Cash, digital payments, financial inclusion, payment systems, consumer behavior, cards, mobile payments, fast payments, transparency, payment efficiency
  topic: keywords
creator: Md Kamrul Islam
creator: Shah Farhan Rabbani
creator: Yusuf Oli Rahat
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2025-04-30
publication date: 2025-04-30
starting page: 118
ending page: 134
doi: 10.32996/jefas.2025.7.3.11

AI-Driven Predictive Modeling for Detecting Suspicious Trading Patterns, Anomalous Order Activity, and Market Manipulation in U.S. Equity Markets
type: item
description: Modern U.S. equity surveillance increasingly depends on predictive systems that flag suspicious order placement, cross-venue quote behavior, and abnormal execution patterns before manipulative episodes fully unfold. This paper develops an AI-driven research framework for detecting suspicious trading patterns, anomalous order activity, and market manipulation in U.S. equity markets by combining market microstructure theory, public regulatory evidence, and contemporary machine-learning methods. Drawing on more than forty professional sources, the study positions surveillance as a multimodal prediction problem spanning limit-order-book dynamics, order-routing behavior, venue fragmentation, trader interaction networks, news and disclosure context, and enforcement-informed labels. Market-structure evidence shows that U.S. equity average daily volume rose from an estimated 11.1 billion shares in 2023 to 12.2 billion in 2024 and 17.6 billion in 2025, while off-exchange Trade Reporting Facility share increased from about 45% in April 2023 to 47.0% in 2024 and 50.6% in 2025, underscoring the scale and fragmentation challenge facing surveillance teams. The paper proposes hybrid architecture that integrates gradient-boosted baselines, temporal transformers, and graph neural networks with explainable AI overlays for analyst review. Methodologically, the framework emphasizes severe class imbalance, regime shifts, weak and delayed labels, and cost-sensitive evaluation anchored in precision at alert budgets, time-to-detection, and economic materiality rather than headline accuracy alone. The paper argues that trustworthy surveillance requires predictive power, governance discipline, auditable features, and human escalation aligned with U.S. market-structure reforms. The result is a blueprint for more adaptive and useful market-abuse detection.
subject: Research Article
  topic: section
subject: market manipulation, spoofing, layering, suspicious trading, anomalous order activity, graph neural networks, market surveillance, U.S. equities, explainable AI, weak supervision
  topic: keywords
creator: Shah Farhan Rabbani
creator: Yusuf Oli Rahat
creator: Md Kamrul Islam
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-18
publication date: 2026-03-18
starting page: 13
ending page: 27
doi: 10.32996/jefas.2026.8.4.2

Socio-Economic Impacts and Fiscal Vulnerabilities Following the February 6, 2023 Kahramanmaraş Earthquakes in Türkiye
type: item
description: The two major earthquakes (Mw 7.7 and Mw 7.6) that occurred on 6 February 2023, centered in Kahramanmaraş, produced profound socio-economic impacts in southeastern Türkiye. The disaster directly affected eleven provinces, caused widespread destruction, and resulted in more than 50,000 fatalities, while also leading to significant losses in Syria. Approximately 14 million people were directly affected by the physical destruction, and long-term socio-economic consequences are expected to persist. This study evaluates the direct and indirect economic losses caused by the earthquakes within a comprehensive framework, focusing on their impact on production, foreign trade, labor markets, and public finance. In this context, the Adaptive Regional Input–Output (ARIO) model, which enables time-dependent analysis of post-disaster economic impacts, is employed. The findings indicate that the earthquakes weakened regional production capacity, increased employment losses, and significantly expanded public expenditures, thereby deepening fiscal vulnerabilities. Population mobility has also led to structural changes in labor markets and national economic balances. Although reconstruction activities may support short-term recovery, sustainable growth requires integrated policy strategies that strengthen fiscal resilience, restore employment, and enhance long-term regional economic resilience and stability.
subject: Research Article
  topic: section
subject: Socio-economic impacts, fiscal resilience, ARIO model, economic vulnerability
  topic: keywords
creator: Barış Mert Büyüksaraç
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-18
publication date: 2026-03-18
starting page: 01
ending page: 12
doi: 10.32996/jefas.2026.8.4.1

AI-Driven Predictive Analytics Framework for Anti-Money Laundering Risk Management and Financial Infrastructure Protection in U.S. Banking Systems
type: item
description: U.S. banks run anti money laundering (AML) programs that must detect suspicious activity at scale, document decisions, and file timely Suspicious Activity Reports (SARs) while protecting SAR confidentiality (31 C.F.R. § 1020.320, 2023; FFIEC, 2021). Illicit finance increasingly exploits payment rails and multi account networks, raising compliance exposure and operational risk (FinCEN, 2021). This manuscript proposes a compliance aware, AI driven predictive analytics framework for U.S. banking. The framework integrates three layers: supervised alert ranking and suppression using calibrated gradient boosted trees (Chen & Guestrin, 2016); unsupervised anomaly detection for emerging typologies under label lag (Liu et al., 2008); and graph learning to capture relationship centered laundering structures such as mule rings and layering (Kipf & Welling, 2017; Hamilton et al., 2017). We specify a data and feature pipeline for deposits, wires, ACH, cards, and instant payments, including preprocessing, entity resolution, feature engineering, and class imbalance controls using cost sensitive objectives and precision recall analysis (Elkan, 2001; Saito & Rehmsmeier, 2015). Explainability is embedded as an evidence packet for investigators and validators using SHAP, LIME, counterfactuals, and graph explanations (Lundberg & Lee, 2017; Ribeiro et al., 2016; Wachter et al., 2018; Ying et al., 2019). Privacy preserving options, including federated learning, secure aggregation, and differential privacy, enable cross portfolio learning without centralizing sensitive customer data (Dwork et al., 2006; McMahan et al., 2017). Deployment guidance addresses real time scoring, latency targets, drift monitoring, and integration with case management and BSA e filing.
subject: Research Article
  topic: section
subject: i-Money Laundering, Machine Learning, Graph Neural Networks, Financial Crime, National Security, Explainable AI, Privacy-Enhancing Technologies
  topic: keywords
creator: Md Ibrahim
creator: Sakib Mahmud
creator: Muhaimin Ul Zadid
creator: Nusrat Jahan
creator: Md Moshiour Rahman
creator: A S M FAHIM
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2024-01-25
publication date: 2024-01-25
starting page: 155
ending page: 166
doi: 10.32996/jefas.2024.6.6.12

Design and Implementation of an AI-Augmented Autonomous Financial Operations Framework for Cloud-Native ERP Systems Using SAP BTP and RAP
type: item
description: Enterprise financial systems are undergoing rapid transformation due to increasing regulatory complexity, high-volume digital transactions, and the shift toward cloud-native architectures. Traditional ERP financial modules rely heavily on rule-based validations and manual reconciliation processes, limiting their ability to detect anomalies, prevent revenue leakage, and adapt dynamically to evolving compliance requirements. Despite the robustness of platforms such as SAP S/4HANA, financial operations in large enterprises continue to depend on static validations, post-period reconciliation, and manually supervised exception handling. These constraints increase fraud exposure, revenue leakage risk, and financial close cycle duration. This research introduces the Autonomous Financial Operations Framework (AFOF), a cloud-native, AI-augmented architecture that embeds intelligent automation directly within ERP transactional workflows. The framework integrates: The extensibility and microservices capabilities of SAP Business Technology Platform Behavior-driven service modeling via SAP ABAP RESTful Application Programming Model (RAP) Embedded machine learning services for anomaly detection and predictive analytics The proposed framework leverages the extensibility of SAP S/4HANA, the cloud capabilities of SAP Business Technology Platform, and the service-oriented programming paradigm of SAP ABAP RESTful Application Programming Model (RAP) to create a self-optimizing financial operations layer. The architecture introduces five core components: An Intelligent Posting Validation Engine using behavior-driven RAP logic. An AI-based Anomaly Detection Module for financial irregularities. Automated reconciliation services for high-volume subledger environments. Predictive revenue leakage analytics tailored for subscription-based monetization systems. A secure event-driven extension layer supporting scalable enterprise integration. A controlled enterprise-scale simulation was conduct…
subject: Research Article
  topic: section
subject: Design; Implementation; AI-Augmented Autonomous Financial Operations Framework; Cloud-Native ERP Systems; SAP BTP; RAP
  topic: keywords
creator: Raghavendra Depa
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-03-09
publication date: 2026-03-09
starting page: 38
ending page: 52
doi: 10.32996/jefas.2026.8.3.5

Generative AI and U.S. Financial Reporting Integrity: Detecting Narrative Manipulation, Risk Disclosure Gaming, and Fraud Signals in 10-K Filings
type: item
description: U.S. capital markets rely on high-integrity corporate disclosure, yet the narrative portions of annual reports—particularly Management’s Discussion and Analysis (MD&A) and Risk Factors—remain vulnerable to strategic language management and, increasingly, generative-AI-assisted drafting. Unlike traditional misstatement detection that focuses on accounting ratios, narrative manipulation can distort investor beliefs through selective emphasis, obfuscation, boilerplate recycling, and inflated forward-looking language while remaining difficult to audit at scale. This paper develops a conceptual–methodological framework to measure disclosure integrity and detect narrative manipulation in U.S. 10-K filings. We construct a transparent EDGAR pipeline that parses MD&A and Risk Factors and extracts auditable features capturing (i) financial-context tone and uncertainty, (ii) readability and complexity, (iii) risk-factor novelty versus boilerplate drift (year-to-year similarity), (iv) forward-looking intensity, and (v) “tone–fundamentals gaps” that flag potential inconsistency between narrative claims and contemporaneous performance signals. We benchmark interpretable statistical models (logit/elastic net) against flexible machine-learning specifications while preserving explainability, and we evaluate performance using governance-relevant outcomes including enforcement-linked misstatement events and restatement proxies, complemented by market-based reactions around filing dates. We further test whether narrative-risk signals strengthen in the period associated with broad diffusion of generative AI writing tools. The proposed framework contributes to accounting, finance, and regulatory technology by converting narrative disclosure risk into measurable, monitorable indicators. Findings are positioned to inform SEC disclosure oversight, audit planning, and issuer governance by enabling scalable detection of narrative manipulation and risk-disclosure gaming that may undermine inve…
subject: Research Article
  topic: section
subject: 10-K; MD&A; risk factors; disclosure integrity; textual analysis; narrative manipulation; generative AI; SEC enforcement; audit analytics; market integrity
  topic: keywords
creator: Anika Anjum Pritty
creator: Md Ibrahim
creator: A S M FAHIM
creator: Muhaimin Ul Zadid
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2024-08-15
publication date: 2024-08-15
starting page: 113
ending page: 129
doi: 10.32996/jefas.2024.6.4.11

FinTech for Climate Resilience: Measuring Insurance Gaps, Mortgage Stress, and Household Credit Risk in the United States
type: item
description: Climate-related hazards are increasingly transmitted into household balance sheets through the availability, affordability, and design of homeowners insurance. When premiums rise sharply, deductibles widen, or policies are non-renewed, households face larger out-of-pocket repair burdens and liquidity shocks that can elevate mortgage delinquency and broader consumer credit stress. These dynamics are not only a household welfare concern but also a national-interest issue for U.S. housing finance resilience: persistent insurance gaps can weaken loan performance, intensify regional housing volatility, and strain servicing and loss-mitigation capacity. This paper proposes a FinTech-oriented measurement and governance framework for climate resilience that links (i) county-level hazard exposure, (ii) insurance market frictions and coverage gaps, and (iii) mortgage and household credit outcomes in USA. Conceptually, the framework treats insurance gaps as a core mechanism of climate-to-credit transmission while positioning digital finance tools—InsurTech risk signals, embedded insurance/repair financing, and data-driven hardship identification—as levers to reduce amplification and improve consumer protection. Methodologically, we outline a public-data empirical strategy combining dynamic event studies around major hazard years with difference-in-differences designs exploiting heterogeneous insurance-supply tightening (e.g., non-renewal intensity and premium acceleration). We specify a replicable construct set and proxy map based on U.S. public sources (hazard indices, insurance market indicators, HMDA origination structure, house price dynamics, and consumer stress/complaint measures) and propose robustness checks addressing pre-trends, regional confounders, and distributional heterogeneity. To make the manuscript submission-ready, we include clearly labeled demonstration results generated on a simulated county-year panel calibrated to plausible ranges; these are placeholder…
subject: Research Article
  topic: section
subject: climate resilience; InsurTech; homeowners insurance; non-renewals; mortgage delinquency; household credit risk; consumer protection; financial stability; governance; U.S. housing finance
  topic: keywords
creator: Md Ibrahim
creator: A S M FAHIM
creator: Muhaimin Ul Zadid
creator: Anika Anjum Pritty
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2025-08-12
publication date: 2025-08-12
starting page: 190
ending page: 205
doi: 10.32996/jefas.2025.7.4.15

The Use of Technology and Data Analytics in Modern Auditing: A Systematic Review
type: item
description: The auditing profession is undergoing a significant transformation driven by advancements in technology and data analytics. While these innovations promise enhanced audit quality, efficiency, and reliability, their adoption and effectiveness vary across contexts. This systematic review synthesizes empirical evidence from 2020 to 2025 to examine the types, applications, outcomes, and challenges of technology and data analytics in modern auditing. Following PRISMA guidelines, a comprehensive search was conducted across Scopus, Web of Science, IEEE Xplore, and ScienceDirect, yielding 260 records. After screening, 10 studies met the inclusion criteria. Risk of bias was assessed using the ROBINS-I tool, and findings were synthesized narratively due to methodological heterogeneity. The review highlights the transformative impact of technologies such as big data analytics, artificial intelligence, and federated learning on audit quality and efficiency. Key findings include the positive role of client technological readiness in remote auditing, the moderating effect of cybersecurity on audit data analytics, and the challenges of skill gaps and resource constraints in small and medium-sized practices. However, benefits are context-dependent, with emerging markets facing unique regulatory and infrastructural barriers. While technology and data analytics offer substantial benefits for auditing, their successful implementation requires tailored strategies that address contextual, technical, and human factors. Future research should prioritize longitudinal and comparative studies to bridge the gap between experimental promise and practical application.
subject: Research Article
  topic: section
subject: auditing, technology, data analytics, artificial intelligence, systematic review, audit quality
  topic: keywords
creator: Rasha Fallatah
rights: Copyright (c) 2026 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-04-13
publication date: 2026-04-13
starting page: 65
ending page: 78
doi: 10.32996/jefas.2026.8.4.5

A study of Implicit disagreement in MPC meetings for selected Eastern European Economies using the Taylor Rule
type: item
description: This paper in an empirical attempt to study implicit disagreement among MPC members of central banks contained in the minutes of the MPC meetings for three Eastern European economies, viz., Poland, Czech Republic and Hungary. NLP sentiment analysis techniques, specifically a BERT model trained and fine-tuned on the corpus of the text of the minutes of the MPC meetings of the three countries, are applied to extract sentence level sentiment scores in terms of hawkish, dovish or neutral sentiment which are then used to calculate meeting level Disagreement Index(DI) variable by taking the Standard Deviation of the sentence level sentiment scores. We augment the standard Taylor rule formula for setting monetary policy rates with this DI variable and see that it impacts the interest rate negatively, suggesting that higher implicit disagreement might restrain the MPC members from being too hawkish which might be influenced by higher uncertainty about economic conditions, especially fears of a looming recession. So, we find that disagreement has a significant impact, even implicit disagreement, on the policy rate decisions of the MPC members. This offers insights into why central banks might hesitate to raise rates immediately in face of higher inflation or output gap pressures.
subject: Research Article
  topic: section
subject: Monetary Policy Committee
  topic: keywords
subject: Implicit measure of disagreement
  topic: keywords
subject: NLP
  topic: keywords
subject: BERT method
  topic: keywords
subject: Tobit Model
  topic: keywords
creator: Spandan Banerjee
creator: Rajendra N. Paramanik
rights: Copyright (c) 2026 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-22
publication date: 2026-02-22
starting page: 30
ending page: 37
doi: 10.32996/jefas.2026.8.3.4

A Deterministic Trajectory-Level Evaluation Framework for Learning-Based Agentic Systems
type: item
description: In the recent past, learning-based agentic systems are being increasingly used to tackle complex decision environments where reliability, transparency, and compliance with governance are key. However, the dominant evaluation strategies are mostly outcome-centric, with a focus on aggregate performance measures like accuracy, rewards, or task completion rates. These evaluation strategies provide little information on the internal decision-making processes that lead to the observable outcomes, especially for reasoning-based agentic systems with multiple steps. This paper proposes a Deterministic Trajectory Level Evaluation Framework (DTLEF) for learning-based agentic systems. The proposed framework transforms the evaluation paradigm from traditional outcome-oriented metrics to an evaluation process that focuses on the trajectories of states and actions within controlled execution conditions. The DTLEF integrates standardized states, comprehensive action trace logging, deterministic replay validation, and behavior verification against governance constraints. The evaluation process, in which the agentic system runs in controlled inference mode and the trajectory traces are compared, enables the identification of policy instabilities, reasoning drift, and non-deterministic behavior. Moreover, the evaluation process also ensures that the trajectories are consistent with the predefined constraints. Unlike traditional performance metrics, the proposed framework does not depend upon empirical data or domain-specific metrics. Instead, it provides a methodology for evaluating agentic systems, including autonomous decision pipelines, tool-enhanced language agents, and cyber-physical control systems, at an architecture level. The proposed framework increases transparency, reproducibility, and compliance without modifying training procedures. This research provides a scalable and domain-independent evaluation methodology for validating learning-based autonomous agents in environme…
subject: Research Article
  topic: section
subject: Agentic Systems, Trajectory-Level Evaluation, Deterministic Replay, Learning-Based Autonomous Agents, AI Governance and Reliability
  topic: keywords
creator: Prasad Maderamitla
creator: Subba Rao Katragadda
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-17
publication date: 2026-02-17
starting page: 25
ending page: 29
doi: 10.32996/jefas.2026.8.3.3

Impact of ERP-Enabled Digital Transformation on Cost Accounting Practices in Saudi Firms
type: item
description: Enterprise Resource Planning (ERP) systems are major instruments of organizational digital transformation as they integrate operational and financial processes and uplift the overall performance. This research focuses on the effect of ERP implementation on cost accounting activities in Saudi firms, the mediating roles of process integra-tion and automation being the primary emphasis. Information was obtained from 60 companies with a total of 290 employees from the manufacturing, construction, retail, energy, and service sectors. Reliability and validity were established through Cronbachs alpha, composite reliability, and average variance extracted. For data analysis, multiple regression, independent t, tests, and Partial Least Squares Structural Equation Modeling were utilized. Findings suggest that the adoption of ERP leads to a significant improve-ment in cost accuracy, cost control effectiveness, variance analysis quality, and managerial decision, making. In addition, process integration and automation partially mediate these relationships, thus advocating the strategic ERP adoption aligned with Saudi Vision 2030 goals.
subject: Research Article
  topic: section
subject: Cost Accounting, ERP, Digital Transformation, Integration, cost accuracy, cost control effectiveness, variance analysis quality, managerial decision making
  topic: keywords
creator: Trabelsi Slaheddine
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-16
publication date: 2026-02-16
starting page: 12
ending page: 24
doi: 10.32996/jefas.2026.8.3.2

Machine Learning Models for Detecting Hidden Collusion Networks in U.S. Corporate Finance
type: item
description: Hidden collusion networks in U.S. corporate finance present a significant challenge to market integrity and regulatory oversight. Such networks are difficult to detect due to the indirect and distributed nature of collusive behavior, which is often embedded within legitimate financial transactions and governance relationships. This study aims to develop and evaluate machine learning models capable of detecting latent collusion structures by combining firm-level financial indicators with relational and governance-based network information. The goal is to uncover clusters of firms that exhibit coordinated behavior while providing interpretable insights for regulatory decision-making. A multi-stage detection framework was implemented, incorporating classical machine learning classifiers, tree-based ensembles, graph neural networks (GNNs), and hybrid models combining feature-driven and structure-driven learning. Corporate entities were represented as nodes within financial and governance networks, with edges encoding ownership ties, shared executives, transactional dependencies, and temporal co-movements. Models were evaluated using precision, recall, F1-score, AUC-ROC, AUC-PR, and network consistency metrics, while robustness analyses examined performance under class imbalance and sparse labeling conditions. Graph-based models outperformed traditional baselines, achieving F1-scores up to 0.91 and AUC-ROC values up to 0.94. Hybrid ensembles that combined tree-based and graph-based predictions achieved the highest overall performance (F1-score = 0.93, AUC-ROC = 0.95, AUC-PR = 0.91). The models successfully identified densely connected collusive clusters and key hub firms, highlighting the importance of relational and temporal features over isolated firm-level indicators. Ablation studies confirmed that financial metrics alone were insufficient to detect coordinated behavior without network structure. The study demonstrates that machine learning, particularly when integra…
subject: Research Article
  topic: section
subject: Machine Learning
  topic: keywords
subject: Collusion Detection
  topic: keywords
subject: Corporate Finance
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Financial Networks
  topic: keywords
subject: Regulatory Analytics
  topic: keywords
creator: Atika Dola
creator: Sakera Begum
creator: Umama Khanom Antara
creator: MD Rahimul Islam
creator: Tasmia Sultana
creator: Nagma Zabin
rights: Copyright (c) 2024 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
date: 2024-02-13
publication date: 2024-02-13
starting page: 143
ending page: 154
doi: 10.32996/jefas.2024.6.1.14

Real-Time Payments and Real-Time Fraud: A U.S. FinTech Risk Framework for RTP Rails and Consumer Protection
type: item
description: Real-time payment (RTP) rails convert retail payments from a batch, revocable process into an always-on, irrevocable, data-rich transfer of value. In the United States, this transition accelerated during 2012–2023 as mobile wallets, account-to-account (A2A) transfers, and instant credit transfers expanded, culminating in nationwide rail competition between private-sector networks and the Federal Reserve’s FedNow Service. The same design features that make RTP economically attractive—immediacy, finality, and API-based integration—also magnify fraud externalities and operational-resilience demands. Authorized push payment (APP) scams exploit consumer initiation and social engineering, while mule accounts, synthetic identities, and rapid cash-out compress detection windows from days to seconds. Regulatory liability regimes (e.g., U.S. Regulation E) were built around unauthorized transfers and do not fully internalize APP harms, creating incentives that may underinvest in preventive controls. This paper develops a pragmatic–institutional risk framework for U.S. RTP ecosystems that integrates (i) fraud typologies and externalities, (ii) operational-resilience requirements for 24/7 settlement, and (iii) consumer-protection and governance mechanisms spanning federal and state authorities. Methodologically, we specify a replicable measurement architecture using a simulated institution–region–year panel (2012–2023) that combines RTP intensity, fraud loss proxies, incident-response performance, and governance strength indicators. We outline identification strategies suitable for U.S. data constraints—fixed-effects panels, staggered adoption difference-in-differences, and instrumented uptake using broadband penetration and legacy payment frictions. Illustrative estimates show that RTP penetration is associated with a statistically meaningful rise in reported scam losses and complaint intensity in early adoption phases, while institutions with stronger control stacks—payee conf…
subject: Research Article
  topic: section
subject: Real-time payments; FedNow; RTP network; authorized push payment scams; fraud externalities; operational resilience; consumer protection; U.S. financial regulation; payment governance
  topic: keywords
creator: A S M FAHIM
creator: Anika Anjum Pritty
creator: Md Ibrahim
creator: Tabasum Akter Tania
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2024-12-25
publication date: 2024-12-25
starting page: 134
ending page: 149
doi: 10.32996/jefas.2024.6.6.11

Algorithmic Accountability in U.S. Consumer FinTech: Governance Mechanisms for Credit Risk, Fair Lending, and Financial Stability
type: item
description: Financial technology (FinTech) has become a core component of the United States financial system, reshaping payment infrastructure, consumer credit markets, and data-driven financial intermediation. While FinTech innovation has improved efficiency and expanded financial access, its rapid scaling has also introduced new sources of systemic risk, regulatory fragmentation, and consumer vulnerability—particularly as artificial intelligence increasingly governs financial decision-making. This study examines FinTech innovation through an integrated governance lens, focusing on how AI-enabled intermediation interacts with financial stability and public-interest outcomes in the United States. Using a multi-source, institutionally grounded panel dataset spanning 2012–2022, the analysis conceptualizes FinTech as part of national financial infrastructure rather than as a peripheral technological disruption. The empirical strategy combines a composite measure of FinTech intensity with indicators of explainable AI adoption and institutional governance strength to assess their joint effects on financial stability risk and public-interest performance. Fixed-effects panel models, interaction specifications, and robustness checks are employed to isolate institutional and temporal dynamics. The results reveal three central findings. First, greater FinTech intensity enhances intermediation efficiency and financial inclusion but is associated with elevated short-term financial stability risks when deployed without adequate oversight. Second, the adoption of explainable AI significantly moderates these risks by reducing algorithmic opacity and improving auditability and supervisory visibility. Third, governance capacity—reflected in regulatory coordination, disclosure mandates, and supervisory engagement—emerges as a decisive factor in aligning technological innovation with systemic resilience and consumer trust. These findings suggest that the principal challenge facing the U.S. financ…
subject: Research Article
  topic: section
subject: FinTech, artificial intelligence, financial stability, governance, algorithmic accountability, consumer protection, United States
  topic: keywords
creator: A S M FAHIM
creator: Md Ibrahim
creator: Anika Anjum Pritty
creator: Tabasum Akter Tania
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2023-08-25
publication date: 2023-08-25
starting page: 80
ending page: 93
doi: 10.32996/jefas.2023.5.4.8

Trade Realignments, Exchange Rate Volatility, and Financial Stability in Sub-Saharan Africa
type: item
description: This study examines the impact of trade realignment, exchange rate volatility, and financial depth on financial stability in Sub-Saharan Africa. Recognizing the critical role of financial stability in promoting economic growth and resilience, the study aims to analyze both the long-run and short-run dynamics of macro-financial interactions and provide evidence-based policy insights. The scope of the study covers a panel of 40 Sub-Saharan African countries over the period 2000 to 2024. A Panel Vector Error Correction Model (VECM) is employed to capture the cointegration relationships and short-run adjustments among financial stability, trade realignment, exchange rate volatility, GDP growth, inflation, and financial depth. The findings reveal a significant long-term relationship between financial stability and the explanatory variables, with deviations from equilibrium gradually corrected over time. In the short run, financial stability responds to lagged effects of exchange rate volatility, trade realignment, financial depth, GDP growth, and inflation, highlighting the delayed transmission of shocks. The joint significance of these variables confirms that short-run fluctuations in financial stability are influenced by their combined effects rather than isolated changes. The study recommends enhancing financial sector depth, carefully managing trade realignments, mitigating exchange rate volatility, controlling inflation, and coordinating macroeconomic and financial policies to strengthen resilience. Policy implications include integrating trade and financial sector policies, implementing forward looking risk management frameworks, and promoting regional coordination to reduce cross-border vulnerabilities. Future research should explore the role of digital finance, country-specific heterogeneity, external shocks, and institutional quality in shaping financial stability in Sub-Saharan Africa.
subject: Research Article
  topic: section
subject: Financial Stability, Trade Realignment, Exchange Rate Volatility, Financial Depth, GDP Growth, Inflation
  topic: keywords
creator: Nicholas Bamegne Nambie
creator: Philomena Dadzie
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-20
publication date: 2026-01-20
starting page: 26
ending page: 40
doi: 10.32996/jefas.2026.8.2.3

Mega-Sporting Events and Soft Power: Perceived Changes in Qatar’s Nation Brand Following the 2022 FIFA World Cup
type: item
description: Countries increasingly use mega-sporting events as instruments of soft power and nation branding, yet empirical evidence of their impact on international perceptions remains limited, particularly in non-Western contexts. This study examines changes in Qatar’s nation brand following the 2022 FIFA World Cup by assessing how international audiences evaluated key nation-brand dimensions before and after the event. Drawing on Anholt’s Nation Brand Index framework, a quantitative, perception-based design was employed using a cross-sectional survey of an international sample. Respondents retrospectively assessed Qatar across six dimensions: governance, culture, people, tourism, exports, and immigration and investment. The findings indicate a statistically significant but modest improvement in Qatar’s overall international image. Perceptual gains were most evident in the dimensions of people, culture, and governance, while economically oriented dimensions showed limited or no change. These results suggest that the World Cup functioned as a reputational enhancer rather than a transformative nation-branding intervention, generating selective and uneven effects. The study contributes to nation-branding and sports-diplomacy research by providing perception-based evidence from a non-Western context and highlighting the importance of aligning mega-events with long-term strategic initiatives to achieve sustainable soft-power outcomes.
subject: Research Article
  topic: section
subject: Nation Branding, Soft Power, Nation Brand Index, Mega Sporting Events, Qatar
  topic: keywords
creator: Mohammed A. S. Ibrahim
creator: Vardan Aleksanyan
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-19
publication date: 2026-01-19
starting page: 14
ending page: 25
doi: 10.32996/jefas.2026.8.2.2

The Impact of Shanghai Cooperation Organization Membership on Pakistan’s Manufacturing Exports: A Difference-in-Differences Gravity Analysis
type: item
description: This study examines the impact of Pakistan’s membership in the Shanghai Cooperation Organization (SCO) on its manufacturing export performance, employing a Difference-in-Differences (DID) framework within a gravity-type trade model. Using panel data covering pre- and post-integration periods, the analysis compares Pakistan’s manufacturing exports to SCO member countries with those to non-SCO trading partners to identify the causal effect of SCO accession in 2017. Descriptive evidence indicates a gradual but more stable growth in manufacturing exports following SCO membership, with notable sectoral variation across industries. Empirical results reveal a positive and statistically significant effect of SCO membership on Pakistan’s manufacturing exports, confirming that regional integration has enhanced trade performance beyond pre-existing trends. The validity of the DID approach is supported by pre-treatment parallel trend tests, while robustness checks including placebo tests and Poisson Pseudo-Maximum Likelihood (PPML) estimation confirm the consistency of the findings. Heterogeneity Sectoral analysis shows stronger export growth in healthcare-related products, whereas traditional sectors such as textiles, minerals, and rubber exhibit relatively modest gains, reflecting ongoing structural constraints. Overall, the findings suggest that SCO integration has contributed positively to Pakistan’s manufacturing export expansion, though the benefits remain uneven across sectors. The study underscores the importance of complementary domestic policies, industrial upgrading, and trade facilitation measures to fully realize the export potential offered by regional economic integration.
subject: Research Article
  topic: section
subject: SCO; Manufacturing Exports; Difference-in-Differences; Regional Integration; Gravity Model; Pakistan
  topic: keywords
creator: Reza Md Masum
creator: Ashutosh Khatri Chhetri
creator: Dan Qu
creator: Muhammad Waseem Shahzad
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-18
publication date: 2026-01-18
starting page: 01
ending page: 13
doi: 10.32996/jefas.2026.8.2.1

Bridging Prediction and Profit: Deep Learning models with Trading Evaluation for FTSE 100
type: item
description: This study examines stock price forecasting for FTSE 100 companies using deep learning and XAIl. The research addresses the disconnect between predictive accuracy and interpret ability in financial models by integrating data-driven forecasting with transparent feature attribution. Four neural architectures: LSTM2, Bi-LSTM3, GRU4 and CNN5 are compared to classical benchmarks: SMA6 and EMA7. Models are trained on OHLCV8 data augmented with technical indicators. Evaluation uses a threshold-based trading strategy. The findings indicate that a lower prediction error does not necessarily result in higher profitability. Although LSTM achieved the lowest prediction error, GRU and Bi-LSTM produced more stable cumulative returns (16%), compared to the EMA benchmark (2%). SHAP9 analysis demonstrates that recent price movements and momentum indicators, particularly SMA, drive model decisions.
subject: Research Article
  topic: section
subject: FTSE 100, LSTM, GRU, Explainable AI, SHAP, Trading strategy, Stock market prediction, BILSTM, financial data, machine learning
  topic: keywords
creator: S. Ehsan Hosseiny N.
creator: Daqing Chen
rights: Copyright (c) 2026 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-15
publication date: 2026-01-15
starting page: 49
ending page: 60
doi: 10.32996/jefas.2026.8.1.4

Trends and Gaps in the Literature about Green Washing in Asia
type: item
description: This bibliometric study explores the trends and gaps in the literature on greenwashing in Asia, analyzing key research patterns, thematic clusters, and geographical contributions. The results highlight an increasing scholarly focus on greenwashing, particularly from 2021 onwards, reflecting growing awareness of environmental issues and deceptive corporate sustainability practices. China and India emerge as leading contributors, with institutions like Shandong University and Massey University playing prominent roles in advancing the research. Thematic clusters emphasize topics such as corporate social responsibility, green marketing, sustainable development, and circular economy, with "greenwashing" serving as a central concept linking these themes. Despite the rising volume of research, gaps persist in understanding the effectiveness of regulatory frameworks, consumer perceptions, and cross-sectoral impacts of greenwashing. This study underscores the need for interdisciplinary approaches and actionable strategies to address greenwashing, supporting authentic sustainability practices and policies.
subject: Research Article
  topic: section
subject: Greenwashing
  topic: keywords
subject: Sustainability
  topic: keywords
subject: Corporate Social Responsibility
  topic: keywords
subject: Sustainable Development
  topic: keywords
subject: Asia
  topic: keywords
subject: Bibliometric Analysis
  topic: keywords
creator: Andi Silvan
creator: Rudi Zulfikar
creator: Lia Uzliawati
creator: Yeni Januarsih
rights: Copyright (c) 2026 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-24
publication date: 2026-01-24
starting page: 01
ending page: 11
doi: 10.32996/jefas.2026.8.3.1

Reconstruction as a Lever for Sustainable Development in Gaza: Between Reality and Prospects for Recovery After the 7 October 2023 War
type: item
description: The 2023 conflict in the Gaza Strip resulted in unparalleled human, material, and structural damage, valued at approximately USD 18.5 billion, equivalent to almost a full year of Palestinian GDP. Beyond the extent of physical devastation, the hostilities revealed the profound vulnerability of Gaza’s economy, public systems, and communal structure, and severely aggravated hardship, joblessness, and involuntary movement. This analysis explores how post-2023 rebuilding can surpass a brief humanitarian reaction to become a strategic instrument for enduring progress in Gaza. Employing a descriptive–analytical and specific-example method, the research utilizes recent evaluations by the World Bank, the UN, and ESCWA (2024–2025) to chart the extent and sectoral nature of the harm, pinpoint major organizational, fiscal, and political barriers, and dissect the connections between rebuilding and the economic, societal, and ecological foundations of permanence. The paper suggests a conceptual model that regards reconstruction as an evolving procedure rooted in “build back superior” tenets, strong local administration, and alignment with the 2030 Agenda. It then details a policy plan for 2025–2035 connecting accommodation, essential structures, earning potential, welfare provisions, rubble handling, and clean power into a unified national blueprint. The paper finishes by asserting that progress in Gaza will only serve as a true impetus for lasting development if it revitalizes populations, the marketplace, and organizations concurrently, under a domestically controlled structure that harmonizes emergency assistance with protracted structural changes.
subject: Research Article
  topic: section
subject: Reconstruction; Gaza Strip; 2023 conflict; Enduring progress; Post-conflict recuperation; Relief effort; Durability; Leadership; Rubble handling; Clean economy; Societal fairness.
  topic: keywords
creator: Walaa Saeed Ismail Alhemdiat
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-11
publication date: 2026-01-11
starting page: 26
ending page: 48
doi: 10.32996/jefas.2026.8.1.3

How Smart City Initiatives Economically Affect Small and Medium-Sized Enterprises (SMEs): Innovation Capacity and Competitiveness – A Comparative Study of Japan and the UK
type: item
description: This paper will look at the impact of smart city projects on the competitiveness and economic performance of small to medium enterprise (SMEs) in the United Kingdom and Japan. The selection of a qualitative comparative research design to be used was based on the systematic analysis of the academic literature, national policy papers and institutional reports. The results indicate that the Japanese long-term, highly centralized type of governance helps in facilitating systemic coordination, structural integration, whereas the UK model that is decentralized, market-driven encourages flexibility, fast experimentation, and data-driven innovation. Nevertheless, having differences, both models unveil that smart city programs can only be translated into the competitiveness of SMEs in case of coherent policies, digital literacy, and collaborative ecosystems. The research has implications on policymakers who want to reconcile technology change with an inclusive economic development in the urban settings.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Smart cities, Small and medium-sized enterprises (SMEs), Innovation and competitiveness, Society 5.0 (Japan), Digital transformation, Open data and innovative governance, Comparative economic analysis (Japan–UK)
creator: Danial Kiani
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-07
publication date: 2026-01-07
starting page: 43
ending page: 57
doi: 10.32996/jefas.2025.7.8.5

Tourism as a Catalyst for Economic Diversification in Saudi Arabia: Vision 2030 and Beyond
type: item
description: Saudi Arabia’s Vision 2030 reform program positions tourism as a cornerstone for economic diversification, aiming to reduce reliance on oil revenues and foster a knowledge-based economy. This paper critically examines the evolution of Saudi Arabia’s tourism sector under Vision 2030, analyzing its economic, social, and cultural impacts while identifying key challenges and opportunities. Drawing on secondary data from the Saudi Ministry of Tourism, OECD, UNWTO, and World Bank, the study reviews flagship projects such as NEOM, the Red Sea Project, and Diriyah Gate. It also explores the role of technology, sustainability, and global partnerships in shaping the sector’s future. The paper concludes with a comprehensive policy framework and actionable recommendations to ensure sustainable, inclusive, and competitive tourism growth by 2030.
subject: Research Article
  topic: section
subject: Tourism, Economic Diversification, Vision 2030, Saudi Arabia, NEOM, Red Sea Project, Sustainability, Cultural Heritage, Employment, Smart Tourism
  topic: keywords
creator: Danial Kiani
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-07
publication date: 2026-01-07
starting page: 13
ending page: 25
doi: 10.32996/jefas.2026.8.1.2